Biology subjects

Cao, S.

Publications and source records attributed to Cao, S..

At least 37 records · Page 2Linked to original sources

bioRxiv2026

A Single-Cell Temporal Atlas of Mouse Nasal Embryonic Development

The mammals breathe and perceive odors through anatomically and functionally distinct respiratory and olfactory regions of the nose; however, how the early embryo endows this system remains largely unknown. Here, we present a temporal high-resolution single-cell RNA sequencing (scRNA-seq) atlas of mouse nasal development, comprising 183,247 cells collected from 20 embryos spanning embryonic day 10.5 (E10.5) to E18.5. We identified 7 major cell clusters and 52 molecularly defined subtypes, including previously unrecognized rare populations and niche-derived signaling components involved in progenitor cell maintenance. Lineage reconstruction of epithelial and mesenchymal compartments revealed dynamic cell fate transitions and highlighted candidate regulators of lineage specification, including Foxa1 as a regulator of nasal respiratory ciliated epithelium differentiation. Integration with adult nasal epithelium datasets further delineated transcriptional dynamics of olfactory stem cells. This study provides a molecular roadmap of nasal development and offers a valuable resource for future research into nasal biology and regenerative strategies.

developmental biology↗

bioRxiv2026

Cost-effective hybrid long- and short-read sequencing enables accurate somatic structural variant detection

Somatic structural variant (SSV) calling typically requires matched normal data. Incorporating relatively inexpensive short-read sequencing not only provides essential germline information but can also replace a substantial portion of long-read sequencing, thereby enabling more cost-effective somatic SV detection. Here, we present SomaSV, a hybrid sequencing framework that integrates 30x tumor long-read data with matched normal data comprising 10x long-read and 30x short-read sequencing. This design achieves high-accuracy somatic SV detection while remaining cost-competitive. Comprehensive benchmarking demonstrates that SomaSV outperforms current state-of-the-art methods by more than 13% in F1 score while reducing sequencing costs by approximately 19%. Moreover, SomaSV identifies clinically relevant cancer-associated genes, including CLDN4 and ROBO2, highlighting its potential for discovering valuable biomarkers to support early cancer screening and diagnosis. The source code for SomaSV can be accessed at https://github.com/eioyuou/SomaSV.

bioinformatics↗

bioRxiv2026

In Vivo Surface Reconstruction of Three Signal-defined Intracortical Layers Using 5T 3D FLAIR MR Imaging

BackgroundIn vivo whole-cortex quantification of intracortical signal-defined layering on the routinely acquired structural MRI remains limited. PurposeTo develop and validate an automated framework to reconstruct three intracortical signal-defined layers from 5T three-dimensional (3D) T2-weighted fluid-attenuated inversion recovery (FLAIR) and to characterize whole-cortex morphometrics and regional organization across a prespecified cortical organizational framework. Materials and MethodsIn this retrospective study, 5T 3D FLAIR images were acquired between February and July 2024. Brain Multi-Layer Surface Reconstruction (BrainMLSR) reconstructed three intracortical signal-defined layers, and derived intracortical layer thickness and surface area measures and ratios. Performance was evaluated against manual annotations and assessed for test-retest repeatability (n=13) and cross-site feasibility (n=2). Paired two-tailed t-tests and linear mixed-effects models were used. A proof-of-concept analysis compared Heschls gyrus ratios between 19 patients with temporal lobe epilepsy (TLE) and 19 age-matched healthy controls (HC). ResultsA total of 270 healthy participants (mean age, 54.4{+/-}14.5 years; 146 men) were included. Agreement with manual hypointense-layer annotations was high (Dice, 0.960{+/-}0.003), and was similar in the cross-site dataset (Dice, 0.954{+/-}0.009). In the test-retest dataset, average symmetric surface distance was less than 0.1 mm. Across prespecified systems, thickness and surface area ratios varied by region; within an auditory-perisylvian hierarchy, banksSTS showed a localized turning point with an increased hyperintense layer thickness ratio and decreased hypointense layer thickness ratio, accompanied by inflections in surface area ratios (P < .001). In bilateral Heschls gyrus, hypointense (left: 0.619{+/-}0.262 vs 0.881{+/-}0.102; right: 0.607{+/-}0.310 vs 0.907{+/-}0.141 mm) and isointense (left: 0.406{+/-}0.225 vs 0.678{+/-}0.128; right: 0.478{+/-}0.232 vs 0.808{+/-}0.176 mm) layer thicknesses were lower in TLE than in HC (all P<.001). ConclusionBrainMLSR enabled accurate and repeatable in vivo reconstruction of three intracortical signal-defined layers from a single 5T 3D T2-weighted FLAIR acquisition and provided whole-cortex boundary-based morphometry with interpretable regional organization. Key ResultsO_LIIn this retrospective study of 270 participants, Brain Multi-Layer Surface Reconstruction (BrainMLSR) showed high agreement with hypointense-layer annotations (Dice, 0.960{+/-}0.003). C_LIO_LIWithin an auditory-perisylvian hierarchy, the hyperintense-layer thickness ratio peaked (0.620{+/-}0.003) and the hypointense-layer ratio was the lowest (0.156{+/-}0.002) in the bank of the superior temporal sulcus (P < .001). C_LIO_LIIn a proof-of-concept analysis, patients with temporal lobe epilepsy (vs healthy controls) had higher hyperintense-layer thicknesses ratio in the left hemisphere (0.523{+/-}0.085 vs 0.417{+/-}0.036, P<.001). C_LI SummaryAn automated multiple-layer surface reconstruction framework (BrainMLSR), applied to 5T 3D T2-weighted FLAIR images, produced reproducible whole-cortex, signal-defined laminar morphometry and demonstrated coherent patterns across prespecified cortical organizational framework.

neuroscience↗

bioRxiv2026

MetaKnogic-Alpha: A Hyper-Relational Knowledge Base for Grounded Metabolic Reasoning

The exponential trajectory of biomedical literature has precipitated a fundamental "synthesis gap" in metabolic research, where critical mechanistic insights remain fragmented across hundreds of thousands of disjointed full-text articles, preventing the consolidation of a global mechanistic view. Here, we present MetaKnogic-Alpha, a foundational mechanistic knowledge substrate designed to bridge this gap by transforming unstructured literature into a navigable, logic-based resource. MetaKnogic-Alpha synthesizes over 100K full-text articles into a hyper-relational hypergraph structure, preserving the n-ary relational logic inherent in complex metabolic pathways. To ensure biological rigor, we implemented a hierarchical discovery protocol: an autonomous reasoning agent first enriches query nomenclature for domain-specific precision, followed by a multi-hop topological expansion within the hypergraph to surface functional neighbors, such as enzymatic co-factors and distal regulators, often lost in traditional search paradigms. Crucially, the system subjects all literature-derived insights to a deterministic biochemical grounding against a curated metabolic reaction network, significantly mitigating the risk of probabilistic hallucinations common in standalone generative models. In rigorous benchmarking, MetaKnogic-Alpha achieved a mechanistic accuracy of 0.98 in scenarios where supporting evidence was present, providing a robustly attributable audit trail back to the primary literature via PubMed Central Identifiers. We designate this primary release as "alpha" to establish the foundational architectural logic for a burgeoning million-scale resource. By compressing the synthesis of thousands of papers from a multi-month manual effort into several hours of automated discovery, MetaKnogic-Alpha serves as a high-fidelity research companion that augments the human experts ability to resolve complex metabolic interactions and identify novel therapeutic drivers in precision oncology.

bioinformatics↗

bioRxiv2026

Integrative Single-Cell Epigenomic Atlas Annotates the Regulatory Genome of the Adult Mouse Brain

Histone modifications underpin the cell-type-specific gene regulatory programs that drive the remarkable cellular diversity of the mammalian brain. Here, we jointly profiled four histone modifications and the transcriptome in 2.5 million nuclei from the adult mouse brain. By integrating these data with chromatin accessibility, DNA methylation, and three-dimensional genome organization, we constructed a unified epigenomic atlas spanning over 100 brain cell subclasses, and assigned active, primed, and repressive chromatin states across 81% of the genome. Active chromatin states marked by combinatorial histone modifications better nominate candidate enhancers with functional support than chromatin accessibility alone, while H3K27me3- and H3K9me3-marked chromatin states delineate distinct modes of cell-type-specific gene repression. Finally, this multimodal resource enables deep learning models that predict cell-type-specific epigenomic features and gene expression from DNA sequence, providing a comprehensive framework for annotating the brain regulatory genome and interpreting non-coding disease risk variants. HIGHLIGHTS- A single-cell epigenome atlas of transcription and four histone modifications across 2.5M mouse brain cells - Multimodal integration maps chromatin states across [~]81% of the adult mouse brain genome - Cell-type-resolved chromatin landscapes reveal regulatory programs mediated by enhancers, Polycomb and H3K9me3 - Deep learning models predict cell-type-specific epigenomic features and gene expression from DNA sequence

genomics↗

bioRxiv2026

Single-Cell Atlas of Transcription and Chromatin States Reveals Regulatory Programs in the Human Brain

Directly measuring chromatin states alongside transcription is essential for understanding how cell-type-specific regulatory programs are established and maintained in the adult human brain. We present a large-scale single-cell multimodal atlas generated by jointly profiling transcriptome with active (H3K27ac) and repressive (H3K27me3) histone modifications across 18 brain regions. We profile >750,000 nuclei spanning 160 cell types and integrate these data with chromatin accessibility, DNA methylation, 3D genome architecture, and spatial transcriptome. This framework annotates >500,000 regulatory elements and resolves cell-type-specific chromatin states. We link enhancers to target genes, infer gene regulatory networks, and classify chromatin interactions, revealing neuron-enriched long-range Polycomb repression of developmental genes. Integrating these maps with GWAS data and sequence-based model prioritizes noncoding variants, effector genes, and vulnerable cell types for neuropsychiatric disorders. Finally, cross-species comparisons show conserved activation but more divergent repression. Together, this study provides a functional reference for interpreting noncoding variants, epigenetic memory, and brain organization. HIGHLIGHTSO_LIJoint single-cell profiling of transcriptomes with active or repressive histone modification in >750,000 nuclei across adult human brain. C_LIO_LIChromatin state annotation of >500,000 candidate cis-regulatory elements distinguishes active enhancers from accessible and Polycomb-repressed regions. C_LIO_LICell-type-resolved regulatory networks and sequence-based deep learning model prioritize functional neuropsychiatric risk variants. C_LIO_LISpatial epigenomic imputation reveals laminar layer-specific Polycomb repression programs. C_LIO_LIIntegration with 3D genome architecture reveals neuron-specific super long-range chromatin loops silencing early developmental genes. C_LIO_LIEvolutionary analysis uncovers conserved active regulatory grammar but divergent repressive landscape. C_LI

genomics↗

bioRxiv2026

Transcriptomics-based modeling of methionine metabolism effectively estimates sample-wise DNA methylation activity and epigenetic aging

DNA methylation is a central epigenetic modification that regulates gene expression, maintains genomic stability, and guides cellular differentiation. However, direct measurements of DNA methylation, such as whole-genome bisulfite sequencing or DNA methylation arrays, are costly and require substantial DNA input, limiting their scalability for large cohorts and their applicability to emerging modalities such as single-cell and spatially resolved transcriptomics. In this study, motivated by the fact that DNA methylation is fundamentally a metabolic process, we investigate whether sample-wise DNA methylation activity can be inferred directly from transcriptomic profiles of genes involved in methionine and one-carbon metabolism. We show that a compact metabolic model comprising seven core reaction steps and 98 genes accurately predicts total DNA methylation activity across matched transcriptomic and methylation datasets from CCLE, TCGA, GTEx, and an independent single-cell multi-omics data set. Building on this framework, we develop Total DNA Methylation Activity (TDMA), a physics-informed neural network-based score that enables robust estimation of DNA methylation activity from bulk, single-cell, and spatial transcriptomics data. We demonstrate that TDMA captures methylation-dependent transcriptional regulation and identifies genes and pathways under epigenetic control. Applying TDMA to GTEx, we further reveal strong associations between the predicted total methylation activity, chronological aging, and established epigenetic clocks. We also demonstrated that TDMA can serve as a transcriptomics-derived epigenetic clock and highlights age-dependent roles of folate and glutathione metabolism in epigenetic aging. Applying TDMA to single cell and spatial transcriptomics data collected from pancreatic adenocarcinoma (PDAC), we identified that methionine metabolism and DNA methylation regulates T cell cytotoxicity in the tumor microenvironment of PDAC. Together, this work establishes a scalable, modality-agnostic framework for estimating DNA methylation activity from transcriptomics and provides new insights into the metabolic regulation of epigenetic aging.

bioinformatics↗

bioRxiv2026

High-PepBinder: A pLM-Guided Latent Diffusion Framework for Affinity-Aware Target-Specific Peptide Design

Peptides, as therapeutic molecules, offer unique advantages in targeting complex protein surfaces, yet their rational design remains limited by the vastness of the sequence space and the constraints of traditional approaches. Here, we propose High-PepBinder, a sequence-only conditional diffusion framework for target-specific peptide generation. Guided by the target protein sequence, High-PepBinder adopts a dual encoder architecture that integrates protein language models (pLMs) with the diffusion model. This approach cascades the peptide generation model with an affinity classifier and enables the generation process to capture affinity-related features of the peptides through lightweight joint optimization. Due to the scarcity of protein-peptide affinity data, we constructed PepPBA, to our knowledge the most comprehensive dataset to date, and established a structure- and physics-based screening pipeline to prioritize top candidates. Results show that High-PepBinder demonstrates competitive performance across multiple peptide generation and affinity-related tasks. For representative targets, including KEAP1, XIAP, and EGFR, the generated peptides preserve key binding geometries and interface patterns of reference peptides in predicted complexes, while maintaining sequence diversity and favorable predicted properties. Overall, High-PepBinder contributes toward a general and sequence-only strategy for peptide design, offering a computational framework for expanding peptide discovery against challenging targets.

bioinformatics↗

bioRxiv2026

TSTScope Unifies Single-Cell Multi-Omics to Identify Functional T Cell States Predictive of Immunotherapy Response

Immune checkpoint blockade (ICB) can produce durable responses in cancer, but reliable predictors of benefit are still lacking. CD8 tumor-specific T cells are essential for ICB efficacy, yet it remains unclear which functional states of these cells determine therapeutic success. To address this, we developed TSTScope, an interpretable deep learning framework that integrates single-cell transcriptomic and T-cell receptor sequencing data to generate unified representations of CD8 T-cell identity. By applying TSTScope to non-small cell lung cancer (NSCLC) datasets, we characterized the gene programs defining tumor specificity and computationally inferred a population of potential TSTs. Crucially, we demonstrate that clinical response is not a product of TST abundance, but is instead governed by their functional state. We derived the MPR score, a metric capturing this functional potential, which proved to be a robust predictor of treatment outcomes. In an independent validation cohort, the MPR score significantly outperformed established biomarkers. Collectively, TSTScope identifies a distinct functional state of tumor-specific T cells as a primary determinant of ICB efficacy, providing both a mechanistic framework and a potent tool for precision immunotherapy.

bioinformatics↗

bioRxiv2025

Single-cell transcriptomic atlas reveals that vascular tissues orchestrate cell fate transitions to initiate adventitious root formation in tomato

O_LIAdventitious root (AR) formation enables vegetative propagation critical for agriculture, conservation, and breeding, yet the cellular basis of AR competence in crop species remains poorly characterized. Most mechanistic studies rely on Arabidopsis, which exhibits constrained AR capacity compared to species dependent on clonal propagation. C_LIO_LIWe constructed a single-cell transcriptomic atlas of tomato (Solanum lycopersicum) AR development comprising 7,920 cells across 12 cell types. Regulatory networks were inferred using hdWGCNA and MINI-EX, validated by RNA in situ hybridization and qPCR. Cross-dataset integration with shoot-borne root initiation data enabled developmental staging, while SATURN-based integration of 118,730 vascular cells from eight species permitted evolutionary comparison. C_LIO_LICytoTRACE analysis revealed that vascular tissues--root stele and phloem--possess the highest developmental potential, supporting a model of latent stem cell-like identity rather than dedifferentiation-based reprogramming. Network analysis identified LEA3 as a central hub gene constitutively expressed throughout AR development, regulated by the DOF transcription factor DOF11. Cross-species integration demonstrated that tomato AR-initiating cells share transcriptomic similarity with woody dicots (Liriodendron, r = 0.46; Eucalyptus, r = 0.42) but not Arabidopsis (r = -0.02), with LEA3-containing macrogene weights highest in basal angiosperms and woody species. C_LIO_LIOur findings establish the DOF11-LEA3 axis as a conserved vascular identity module and suggest that AR competence represents an ancestral program retained in species with active cambial dynamics. These results reframe Arabidopsis as a derived rather than representative model for AR biology and identify molecular targets for enhancing vegetative propagation. C_LI

plant biology↗

bioRxiv2025

Efficient Generation of Humanized Cloned Cattle via Embryonic Stem Cell-Mediated Dual-Round Site-Specific Editing

Transgenic cloning allows genetic modification in cattle, yet its translational potential is constrained by low cloning efficiency and limited gene-editing capacity, due largely to the restricted proliferative ability of somatic donor cells. Pluripotent stem cells (PSCs), with their unlimited self-renewal ability, represent a promising alternative to overcome these bottlenecks. Here, we first established stable bovine epiblast stem cell lines (bEpiSCs) and achieved site-specific integration of the HCSN2-HLF-GFP cassette into the {beta}-casein locus, with high transfection (72.07%) and correct integration efficiency (80.48%). Following Cre LoxP mediated excision of the selectable marker, we obtained marker free HCSN2-HLF bEpiSCs that retained pluripotency markers, tri lineage differentiation potential, and supported target protein expression in immortalized bovine mammary epithelial cells. When used as nuclear donors, these cells produced blastocysts with quality comparable to in vitro fertilization (IVF) embryos. Notably, cloning with HCSN2-HLF bEpiSCs resulted in the birth of twelve calves, with significantly higher birth and survival rates (30.71% and 16.74%, respectively) than those obtained using fibroblasts. Both PCR-based genotyping of the edited loci and microsatellite kinship analysis confirmed that all cloned calves were genetically derived from the HCSN2-HLF bEpiSCs. Collectively, this study establishes an efficient embryonic stem cell based platform for targeted gene editing and rapid production of multigene edited cloned cattle, offering a scalable strategy with strong potential for industrial translation.

bioengineering↗

bioRxiv2025

Outer Membrane Vesicles Hijack TIM-1 for Cellular Uptake

Outer membrane vesicles (OMVs) are nanoscale proteoliposomes shed by Gram-negative bacteria that mediate host-pathogen interactions and hold promise as platforms for vaccines and targeted drug delivery. Despite their biological and translational significance, the cellular mechanisms governing OMV entry into host cells remain poorly understood. Here, we demonstrate that E. coli OMVs are internalized by epithelial cells via clathrin-mediated, receptor-dependent endocytosis. Using a high-throughput screen of over 1,500 human single-pass transmembrane proteins, we identify T-cell immunoglobulin and mucin-domain 1 (TIM-1) as a strong OMV-binding receptor. Functional validation revealed that TIM-1 overexpression markedly increased OMV uptake, whereas TIM-1 knockout and antibody-mediated blockade significantly impaired internalization across multiple cell lines. Mechanistic studies demonstrate that TIM-1 binds to lipopolysaccharide (LPS) on the OMV surface via its known phosphatidylserine-binding domain. Uptake of OMVs by TIM-1 triggers proinflammatory cytokine production which can be reduced by preventing this interaction. Additionally, OMVs from multiple bacterial species hijack TIM-1 for entry, making it an intriguing antivirulence strategy. Our findings establish TIM-1 as a critical host receptor mediating OMV uptake and provide a novel approach to modulate vesicle-driven pathogenesis and enhance OMV-based therapies.

microbiology↗

bioRxiv2025

A window trial in metastatic pancreatic ductal adenocarcinoma reveals resistance mechanisms to targeting the KRAS-MEK pathway

Copy number alterations of KRAS, mutated in over 90% of pancreatic ductal adenocarcinomas (PDAC), and MYC occur in 30-40% of PDAC. Here we demonstrate that KRAS and MYC are frequently co-gained and accompanied with worse prognosis in PDAC. In a Window-of-Opportunity clinical trial for metastatic PDAC, serial biopsies and deep multi-omics analyses were utilized to explore resistance mechanisms to MEK inhibition, as a surrogate for KRAS inhibition. Tumors from four of 14 patients showed Ki-67/CA19-9-based biomarker response (BR). Non-BR tumors were enriched for KRAS/MYC co-gain and KRASG12D variant. A transcriptomic signature of BR tumors was inversely correlated with KRASG12D/MYC co-gain in a large PDAC dataset and predictive for KRAS inhibitor response in multiple models. Finally, co-targeting KRAS and MYC was synergistic in KRASG12D/MYC co-gain PDAC. Together, this study provides insight into KRAS inhibitor resistance and supports MYC as an important target to improve patient outcomes in this deadly disease.

cancer biology↗

bioRxiv2025

Advancing Knock-In Approaches for Robust Genome Editing in Zebrafish

Precise genome editing remains a major challenge in functional genomics, particularly for generating knock-in (KI) alleles in model organisms. Here, we introduce the mini-golden system, a versatile Golden Gate-based subcloning platform that enables rapid assembly of donor constructs containing homology arms and a gene of interest. This system offers a library of middle entry vectors including diverse genes, enhancing the preparation of donor minicircles for KI applications. Using the mini-golden system, we efficiently generated a foxd3CreER KI zebrafish line, allowing conditional recombination in neural crest cells. To further improve genome editing precision, we developed a synthetic exon-based donor template strategy combined with fluorescence screening. Using this approach, we successfully engineered a targeted isoleucine-to-valine substitution (Ile-to-Val) in hbaa1.2, one of the two adult hemoglobin alpha genes in zebrafish. Importantly, despite the high sequence similarity between hbaa1.2 and its paralog hbaa1.1, our strategy specifically edited hbaa1.2, demonstrating the effectiveness of the synthetic exon approach. This method minimized undesired recombination and significantly improved the identification of lines carrying the edited genome. Together, we provide a robust toolkit for efficient and precise genome engineering in zebrafish, with broad applicability to other model systems.

developmental biology↗

bioRxiv2025

Orthosteric Molecular Glue Inhibits COP9 Signalosome with Substrate-Dependent Potency

Orthosteric inhibitors block enzyme active sites and prevent substrates from binding. Enhancing their specificity through substrate dependence seems inherently unlikely, as their mechanism hinges on direct competition rather than selective recognition. Here, we show that a molecular glue mechanism unexpectedly imparts substrate-dependent potency to CSN5i-3, an orthosteric inhibitor of the COP9 signalosome (CSN). We first confirm that CSN5i-3 inhibits CSN, which catalyzes NEDD8 deconjugation from the cullin-RING ubiquitin ligases (CRLs), by occupying the active site of its catalytic subunit, CSN5, and directly competing with the iso-peptide bond substrate. Curiously, the orthosteric inhibitor binds free CSN with only micromolar affinity, yet achieves nanomolar potency in blocking its deneddylase activity. Cryo-EM structures of the enzyme-substrate-inhibitor complex reveal that active site-engaged CSN5i-3 occludes the substrate iso-peptide linkage while simultaneously extending an NEDD8-binding exosite of CSN5, acting as a molecular glue to cement the NEDD8-CSN5 interaction. The cooperativity of this tri-molecular CSN5i-3-NEDD8-CSN5 assembly, in turn, sequesters CSN5i-3 at its binding site, conferring high potency to the orthosteric inhibitor despite its low affinity for the free enzyme. Together, our findings highlight the modest affinity requirements of molecule glues for individual target proteins and establish "orthosteric molecular glue inhibitors" as a new class of substrate-dependent enzyme antagonists.

molecular biology↗

bioRxiv2025

MetaBeeAI: an AI pipeline for full-text systematic reviews in biology

The volume and complexity of scientific literature are expanding rapidly, making it increasingly difficult to extract and synthesise information across studies. This challenge is particularly acute in the biological sciences, where evidence spans multiple levels of organisation and heterogeneous experimental designs. Large Language Model (LLM) pipelines offer a scalable route to evidence synthesis, but many existing approaches lack transparency, modularity, and effective mechanisms for human oversight. We present MetaBeeAI, an open-source, modular pipeline that integrates established LLM techniques into a coherent, auditable workflow for structured data extraction in biology. MetaBeeAI combines modular prompting, multi-pass extraction, and expert-in-the-loop validation within an interface that presents model outputs alongside source text, enabling inspection, correction, and iterative refinement. The pipeline produces machine-readable records of prompts, configurations, and expert annotations, supporting reproducibility and continuous improvement. We apply MetaBeeAI to 924 research papers on bees and pesticides, extracting structured information on species, compounds, exposure designs, and experimental context. Evaluation demonstrates improved consistency, convergence with expert judgement, and robustness across heterogeneous biological studies, highlighting the value of expert-guided refinement. MetaBeeAI provides a transparent and extensible framework for scalable evidence synthesis, supporting reliable integration of LLMs into biological research workflows.

ecology↗

bioRxiv2025

An infusible decellularized extracellular matrix material binds to vasculature in infarcted myocardium and induces pro-reparative gene expression following acute myocardial infarction through inherent avidity and bioactive signaling

To treat acute myocardial infarction immediately after reperfusion, we previously engineered an intravascularly infusible decellularized extracellular matrix (iECM) biomaterial that exerts immunomodulatory and pro-reparative effects. However, the impact of the heterogeneous contents of iECM on infarct localization and downstream biological function is unknown. Using liquid chromatography, iECM is separated into a high molecular weight (MW) and low MW component. Mass spectrometry confirms compositional similarity, while biochemical assays and transmission electron microscopy highlight differences in biochemical features and structure, revealing a nanofibrillar high MW component and a globular peptide low MW. Quartz crystal microbalance studies show binding of each component to basal lamina ECM proteins and endothelial cell surface receptors under flow, demonstrating the specificity of ECM biomaterials to permeable vasculature. In vivo, the low MW component reduces vascular permeability, while neither component alone achieves the retention levels of complete iECM. Using single-nucleus RNA sequencing to probe bioactivity, both components elicited comparable angiogenic, immunomodulatory, and pro-reparative transcriptional programs. These findings illustrate that highly coupled materials and biological characterization uncover fundamental behaviors and properties of iECM biomaterials. Additionally, we show the unique binding behavior of iECM to the gaps of permeable vasculature, which could be exploited for future nanomaterial design.

bioengineering↗

bioRxiv2025

Infusible Extracellular Matrix Biomaterial Enhances Cell-Specific Pro-Repair Responses Following Acute Myocardial Infarction

To mitigate the pathological effects of myocardial infarction, we developed and investigated pro-reparative decellularized extracellular matrix (ECM) biomaterials: an intravascularly infusible ECM (iECM). However, the cellular and molecular mechanisms by which iECM mediates repair are unknown because investigations have relied on bulk techniques. Here, we leverage single nucleus RNA sequencing (snRNAseq) to measure pro-repair in various cell types across acute timepoints (1, 3 and 7 days post infusion). In iECM, we found pro-reparative macrophage activation, fibroblast remodeling, increased vasculaure development, lymphangiogenesis, cardioprotection, and neurogenesis. These findings were validated through spatial transcriptomics. Thus, we define the pro-reparative nature of decellularized ECM biomaterials on cardiac cell types and elucidate previously undiscovered therapeutic pathways, further demonstrating the potential of iECM as an MI therapy as well as display the wealth of data generated from next-generation sequencing.

bioengineering↗